Home | Data Overview | Multi-Dimensional Analysis | Models | Conclusion

Major | Gender | Career | Ethnicity | First Generation Students | Dependents | Medical Condition | Medical Care | Analysis

Function

To begin, let’s explore the function we created to run all of this statistical analysis:

mysubsetMDS <- function(x){
  mysubset <- df %>% 
    select(starts_with(x))
  
  meta <- metaMDS(mysubset)
  MDS_df <- data.frame(MDS1=meta$points[,1],MDS2=meta$points[,2]) %>%
    cbind(demo)
  return(MDS_df)
}


In English, this function allows us to run the MDS according to each subset of demographic and each subset of question type that we want. Obviously, in this page, we are exploring medical conditions.

Question Types

Science Identity

Let’s explore the science identity subset of questions first. Running our function we created and plotting it, we are left with this image of the plot:

This is great and all, but let’s run an adonis test to see if there is a significant difference in how different medical conditions responded to science identity questions:

## 
## Call:
## adonis(formula = si ~ demo$med_condition) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                     Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$med_condition   1    0.0416 0.041630  1.3051 0.00419  0.242
## Residuals          310    9.8883 0.031898         0.99581       
## Total              311    9.9299                  1.00000

This shows that the differences in answers are not significant, according to medical conditions.


Career Motivation

Next, let’s look into Carer Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = cm ~ demo$med_condition) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                     Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)  
## demo$med_condition   1    0.2004 0.200409  3.9742 0.01266   0.04 *
## Residuals          310   15.6324 0.050427         0.98734         
## Total              311   15.8329                  1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Look at that! The differences are significant for Career Motivation across presence of medical conditions.


Intrinsic Motivation

Now, Intrinsic Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = im ~ demo$med_condition) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                     Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)  
## demo$med_condition   1    0.1218 0.121815  4.3455 0.01382  0.024 *
## Residuals          310    8.6901 0.028032         0.98618         
## Total              311    8.8119                  1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Look at that! The differences are significant for Intrinsic Motivation across presence of medical conditions.

Self-Determination

Now, Self-Determination:

And an Adonis test:

## 
## Call:
## adonis(formula = sd ~ demo$med_condition) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                     Df SumsOfSqs  MeanSqs F.Model     R2 Pr(>F)
## demo$med_condition   1    0.0145 0.014478 0.59035 0.0019  0.472
## Residuals          310    7.6024 0.024524         0.9981       
## Total              311    7.6168                  1.0000

This shows that the differences in answers are not significant, according to medical conditions.

Self-Efficacy

Now, Self-Efficacy:

And an Adonis test:

## 
## Call:
## adonis(formula = se ~ demo$med_condition) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                     Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$med_condition   1    0.0107 0.010686 0.38996 0.00126  0.618
## Residuals          310    8.4952 0.027404         0.99874       
## Total              311    8.5059                  1.00000

This shows that the differences in answers are not significant, according to medical conditions.

Grade Motivation

Now, Grade Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = gm ~ demo$med_condition) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                     Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$med_condition   1    0.0290 0.029046  1.3122 0.00422  0.225
## Residuals          310    6.8618 0.022135         0.99578       
## Total              311    6.8909                  1.00000

This shows that the differences in answers are not significant, according to medical conditions.

Competency in Science

Now, Competency in Science:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comp ~ demo$med_condition) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                     Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$med_condition   1    0.0401 0.040140   1.156 0.00372  0.292
## Residuals          310   10.7638 0.034722         0.99628       
## Total              311   10.8040                  1.00000

This shows that the differences in answers are not significant, according to medical conditions.

Personal Community Orientation

Now, Personal Community Orientation:

And an Adonis test:

## 
## Call:
## adonis(formula = per_comm_orient ~ demo$med_condition) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                     Df SumsOfSqs   MeanSqs F.Model      R2 Pr(>F)
## demo$med_condition   1    0.0039 0.0038990 0.44102 0.00142  0.595
## Residuals          310    2.7407 0.0088409         0.99858       
## Total              311    2.7446                   1.00000

This shows that the differences in answers are not significant, according to medical conditions.

Science Community Orientation

Now, Competency in Science:

Well crap, something is broke there. Let’s try running a different form of MDS, giving us a similar analysis in a different way. We can interpret this data similarly to how we did before:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comm_orient ~ demo$med_condition) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                     Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$med_condition   1    0.0174 0.017388  1.1781 0.00379  0.304
## Residuals          310    4.5756 0.014760         0.99621       
## Total              311    4.5929                  1.00000

This shows that the differences in answers are not significant, according to major.

Conclusion

Ultimately, what we can understand from all of this is that Career and Intrinsic Motivation questions are answered significantly differently between those who have medical conditions and those who don’t.

In summary, let’s take a look at the running list we are making of which questions were significant and not according to demographics:


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